Security Engineer (D)

This is a 6 – month contract with our client in the Fintech sector

Where the assurance function tests and red teams, this role builds and hardens.

Our client, a high-growth Fintech innovator, is looking for a Manager: AI Security Engineer. This is a hands-on, engineering-focused role dedicated to designing, building, and operating the actual security controls that protect the enterprise AI and Large Language Model (LLM) estate. You will take threat, assurance, and architecture findings and translate them into production-grade defenses—engineering protective guardrails, writing policy-as-code, and securing automated AI pipelines across multiple markets.

Responsibilities include

• AI Defensive Controls Engineering: Design and build protective controls for the AI/LLM estate, including input/output filtering, content moderation frameworks, and AI firewalls.
• Policy-as-Code & Optimization: Develop and maintain policy engines (such as OPA/Rego) governing model access, token handling, and tool/plugin invocation. Continuously tune guardrails to balance security against false-positive rates.
• Secure API & AI Gateways: Configure and secure API/AI gateway architectures, managing authentication, authorization, rate limiting, schema validation, and prompt/response inspection.
• DevSecOps for AI: Integrate AI security controls and reusable libraries into existing CI/CD and MLOps pipelines, managing infrastructure and defenses strictly as code.
• Telemetry & Logging: Implement comprehensive telemetry, prompt/response logging, and abuse detection to proactively track model behaviour and guardrail bypass attempts.
• Assurance Remediations: Partner closely with the AI Red-Teaming/Assurance functions to rapidly close discovered vulnerabilities and align with the OWASP LLM Top 10 and MITRE ATLAS mitigations.

Requirements include

• Experience: 8–10 years in security engineering or software engineering, with at least 5 years dedicated entirely to building and operating security controls. Strong software automation capability (Python preferred) is essential.
• Defensive Engineering Depth: Proven track record building guardrails, working with policy-as-code frameworks (e.g., OPA/Rego), tuning authorization engines, and implementing data-masking patterns.
• Pipeline Integration: Hands-on experience embedding security parameters directly into MLOps pipelines and managing secure API Gateway environments.
• Framework & Compliance Literacy: Strong understanding of Zero Trust architectures, NIST AI RMF, and data regulations (POPIA, GDPR, PCI DSS).
• Education & Certifications: Bachelor's or Master's degree in Computer Science, Software Engineering, or InfoSec. Preferred credentials include:
• Security/Engineering: CISSP, CSSLP, or secure coding certifications.
• Cloud: AWS, Azure, or GCP Security specializations.